Measurement of soybean fatty acids by near‐infrared spectroscopy: Linear and nonlinear calibration methods

Abstract

A key element of successful development of new soybean cultivars is availability of inexpensive and rapid methods for measurement of FA in seeds. Published research demonstrated applicability of NIR spectroscopy for FA profiling in oilseeds. The objectives of this study were to investigate the applicability of NIR spectroscopy for measurement of FA in whole soybeans and compare performance of calibration methods. Equations were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods. Validation results demonstrated that (i) equations for total saturates had the highest predictive ability (r 2 = 0.91-0.94) and were usable for quality assurance applications, (ii) palmitic acid models (r 2 = 0.80-0.84) were usable for certain research applications, and (iii) equations for stearic (r 2 = 0.49-0.68), oleic (r 2 = 0.76-0.81), linoleic (r 2 = 0.73-0.76), and linolenic (r 2 = 0.67-0.74) acids could be used for sample screening. The SVM models produced significantly more accurate predictions than those developed with PLS. ANN calibrations were not different from the other two methods. Reduction in the number of calibration samples reduced predictive ability of all equations. The rate of performance degradation of SVM models with sample reduction was the lowest. a r 2 is determination coefficient, SEP is SE of prediction corrected for bias, d is bias, RPD is relative predictive determinant, PLS is partial least squares, ANN is artifical neural networks, and LS-SVM is Least Squares support vector machines. b Model parameters provide number of latent variables for PLS, number of inputs and neurons in a hidden layer for ANN, and radial basis function bandwidth and complexity regularization parameter for LS-SVM. NIR MEASUREMENT OF SOYBEAN FATTY ACIDS 425 JAOCS, Vol. 83, no. 5 (2006) FIG. 2. Actual vs. predicted concentration plots for saturates and linolenic FA calibrations. Models were tested on sets of 180 (saturates) and 244 (linolenic) samples. The solid line on each plot represents the regression line.

Application of Artificial Neural Networks to Food and Fermentation Technology

“…The electronic nose (sensor responses analyzed by a neural network) achieved success similar to that obtained using the more usual fatty acid analysis by gas chromatography. Similar application in fatty acid analysis of soyabean oil is reported by Kovalenko et al (2006). An artificial neural network model is presented for the prediction of thermal conductivity of food as a function of moisture content, temperature and apparent porosity.…”

Application of Nondestructive Measurement to Improve Soybean Quality by Near Infrared Reflectance Spectroscopy

“…They suggested that these NIRs equations have potential for use in screening for unsaturated fatty acid content in soybean seed oil. Kovalenko et al (2006) also reported that NIR equations for total saturates had high predictive ability (r 2 =0.91-0.94), however unsaturated fatty acids showed low predictive abilities, oleic (r 2 =0.76-0.81), linolenic (r 2 =0.73-0.76), and linolenic (r 2 =0.67-0.74). NIR spectroscopy also has been used determine soybean oil quality such as determination of degradation in frying oil (Yildize et al, 2001;Gerde et al, 2007), peroxide value in oxidized soybean oil (Yildiz et al, 2003), and cis and trans content in hydrogenated soybean oil (Li et al, 1999).…”

Determination of Polar Components in Frying Oils by Fourier-Transform Near-Infrared Spectroscopy

“…PLS provides a dataset with relevant information and reduced dimensionality; this method eliminates data noise to obtain a more accurate and reproducible calibration 13,26 .…”

“…The values of R and RMSEC of the calibration were considered as an indicator in quality evaluation. In general, the larger the values of R and RMSEC are, the more predictive ability of the calibration is obtained 26 .…”